Today, artificial intelligence (AI), machine learning, robots that writers and screenwriters of the last century dreamed of, have gone beyond fantasy and are embodied in feasible business scenarios, becoming a profitable investment. In the financial sector, algorithms are already entrusted with accounting for transactions, detecting fraudulent schemes, assessing customer creditworthiness, planning resources and generating reporting. But on the other hand, the accelerated process of technology development, involves new difficulties and risks.
Several well-known market leaders have already implemented AI technologies in their work. They can be seen almost anywhere – from banks to trading. In this article, we will have a look at how the introduction of AI affects different financial markets.
Automation for Profit
Banks are the first ones to use AI for various reasons. First of all, the fraud can be detected before it actually happens by data cybersecurity algorithms and transactions across all bank portfolios can quickly be verified. Moreover, compared to a real-life specialist, Al has the ability to evaluate faster and more accurately the person who wants to take a loan (a potential borrower) taking into account wider parameters.
The automation of routine processes permits to protect the company from mistakes that an employee can make through inattention. Robots are also capable of performing more functions, reducing costs for the company. Therefore, banks are introducing collection robots that call customers with little debt. According to forecasts, if you entrust AI with up to 30 processes, then you can save a lot of money.
AI is also widely used by financial institutions in order to create chatbots that answer customers’ simple and common questions. Apart from that, the bots possess the ability to even create an investment portfolio based on the preferences and interests of a particular client. These bots prepare detailed expense reports as well if needed.
Another important area in finance where AI is required is regulatory compliance. It monitors and helps comply with legislation, from know-your-customer and anti-money laundering regulations to asset management laws.
AI in Trading
It comes as no surprise that in the modern world, trading has become a popular activity among people. Does not matter whether we are talking about stocks, cryptos or Forex – trading is everywhere. The introduction of artificial intelligence is most vividly seen in Forex. The emergence of automated Forex trading robots made it easier for customers to simplify their tasks – now robots can close and open deals for you. Of course they are not impeccable, and even these robots make mistakes, considering they have no emotions, but the impact of these robots are tremendous.
Implementation of machine learning in the financial field
As we already stated, the whole concept of Machine learning (ML) is based on mathematical and statistical calculations that identify patterns in data sets and predict the possible outcome of a situation.
How does ML work in practice? Well, first of all, it can contribute to the process of identifying previously implicit patterns associated with macroeconomic indicators, credit ratings, third-party auditor data, and the way a company is operating on the Internet. Moreover, in order to minimize errors committed by algorithms, the financial system must fully acknowledge the specifics of AI and coordinate its application process thoroughly. The first step to achieve that consists in designing a well defined guideline which will control the entire procedure of risk management, design, user experience and so on.
In other words, the advantages of technology are colossal. What’s most important, it has the ability to process and analyze a wide range of information, something that practically can not be done by any human being.
Are there any risks involved?
Usually, the introduction of new tools and mechanisms in all the fields are associated with brand new risks as well. This comes as no surprise due to the fact that the company is obliged to face new conditions that have not been present in its practice previously. Sometimes these kinds of risks may involve financial and reputational damage. This statement often leads to a legal question of who should be blamed in the case of an error occurring- an AI developer or a financial specialist.
To make everything clearer, let’s discuss a practical example. Even though the capabilities of AI algorithms are immense, they may not always have the chance to steer clear of bias. For instance, as stated by a historical sample, women, compared to men, were less likely to approve loans in the past decades. Based on the given data, the algorithm will assume females as unreliable borrowers, and probably, will refuse even creditworthy ones. As a result, the settlements of the bank may be seen as gender discrimination which will lead to claims from various regulators.
To sum up, experts predict that the implementation of machine learning will allow financial systems to increase. A person simply doesn’t possess the ability to modify such a volume of data. But this does not mean that AI can completely replace the job done by a living specialist in the financial field.